ArticleScientific reports2025
Investigation of Lacosamide solubility in supercritical carbon dioxide with machine learning models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Machine learning-based prediction of paracetamol solubility and CO₂ density in supercritical systems using artificial rabbits optimization.Scientific reports · 2026Article
- A predictive study of glycaemic reversal in Chinese individuals with prediabetes based on machine learning: a 5-year cohort study.Frontiers in endocrinology · 2026Article
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1 author.
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Abstract
Lacosamide, a widely used antiepileptic drug, suffers from poor solubility in conventional solvents, which limits its bioavailability. Supercritical carbon dioxide (SC-CO₂) has emerged as an environmentally friendly substitute solvent for pharmaceutical processing. In this study, the solubility of Lacosamide in SC-CO₂ was modeled and predicted using several machine learning techniques, including Gradient Boosting Decision Tree (GBDT), Multilayer Perceptron (MLP), Random Forest (RF), Gaussian Process Regression (GPR), Extreme Gradient Boosting (XG Boost), and Polynomial Regression (PR). These models have the ability to model nonlinear relationships. Experimental solubility information within a large span of pressures and temperatures were employed for model training and validation. The findings suggested that all applied models were competent in providing reliable predictions, with GBDT (R
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